Dark Web API Integration: Separate Malware Licenses from Actor Identity

A dark web API can be very useful to provide an interesting observation without necessitating the attribution of who operated the malware. Seller posts, software licenses, and campaign labels, for instance, are distinct objects. If an integration collapses these objects to a single actor record, subsequent lookups can associate attribution that was not established by anyone.

On October 22, 2026, Group-IB published research on the SiriusQT malware. The research discusses both paid and cracked versions of the malware. Group-IB’s researchers found leaked licenses and used license values to group samples into campaigns and operators. The research describes distribution activities peaking in April and May 2026. The date of Group-IB’s research is important: October 22 is the date of research detailing distribution activities of SiriusQT from earlier dates.

Product teams should decide what a common identifier means before combining records. The suggestions that follow are proposed database and review practices. Posts and independent malware research remain separate practices. Group-IB is the source of the SiriusQT research. The suggestions that follow are analyses of the integration practices rather than built-in attribution practices.

Starting with a source observation

Do not combine posts with actors before documenting the observation. Keep the reference of the post, the time it was published, account information of the post if available, and surrounding text. Capture the time the post was recorded.

Was the post a offer for malware? Was the post a support question? Does the post claim a campaign?

Preserve the text that supports the claim.

An advertisement and a campaign report represent distinct observations. A seller can advertise software with no mention of a buyer. Another report can cover attacks using that software family. The two documents do not necessarily show which buyer uses which seller. In these cases, capture the uncertainty rather than assuming an identity to fill in the gap.

Give each identifier a type and scope

Use distinct records for the source account, the malware family, the sample reference, the license fingerprint and the campaign hypothesis. This is a proposed design for the application database. It is not a description of the fields in an API response.

When storing an account identifier, also store the platform. Two users may have the same display name on different forums. A name is a lead to explore, not to merge. Spelling errors happen. Store the original spelling. Normalized searches happen for different reasons. Store them separately.

For each license value, record the source and identify the sample. Protect sensitive data. General alerts should not disclose the entire secret, and should not include unrelated personal data. Analysts in a controlled investigation can compare IDs.

Attribute each campaign label to the report or analyst who created it. A grouping name should not be assumed to be an independent identity.

Make relationships more specific than a match

An account advertised a tool. A sample was reported. A report analyzed a sample, which was found to contain the license.

Each relationship should connect to its observational evidence. Include the reviewer, the assessment date, and any limitations. A second analyst should be able to recognize the reason for the relationship without having to reconstruct the entire analysis.

Consider two samples that contain the same license fingerprint. The first useful result is the relationship of shared-license. Before saying they share an operator, the ID could have been re-distributed or included in a shared build.

Thereafter, analysts can consider related evidence without having to accept an unreviewed claim of identity.

Keep actor merges reversible

Separate the evidence that supports the merger and evidence that makes it uncertain. Evidence of uncertainty should be reviewed prior to consolidation. After merging profiles, keep the evidence that explains both the merge and the separation. Analysts should understand both merging and separating profiles. Also consider downstream effects. Merging profiles can affect watchlists and results of searches, customer alerts and analyst summaries. Make adjustments in these products as well. When merging profiles, engineers should expect corrections and retain the evidence that justifies the merging. Reviewing the evidence allows another analyst to review the merging decision.

Collection and Attribution must be kept separate

Webz.io’s Dark Web API gives structured JSON feeds for Deep and Dark Web Content. Their product page provides context for the source including publication time and author details and site details. They also provide entity enrichments like domains and emails.

This source context can help an application preserve source observations and reclaim related posts. Also, an application can accept findings from different malware analysis. In your application database, having a Dark Web API license fingerprint will NOT result in the extraction of that fingerprint by the Dark Web API. Make sure to preserve the collection context for each of your findings.

The application team defines entity types and reviews and merges entity interactions. Before presenting an enhanced entity profile to your customers, define what responsibilities you are going to assume. Collecting and standardizing data helps in the investigation. It may not resolve all the questions about who operated the malware.

Run relationships tests before release

In an offline test, create a small set of synthetic data. Add two accounts with the same display name on different sites. Ensure that your source data model maintains these source accounts and supporting data until you have evidence to join them.

Next add two synthetic data records with the same license fingerprint to your application database. Do not provide operator information. Search your application for the fingerprint. Both the records should appear and the operators should not be merged. Approve the relationship via your review, and check if supporting observations are accessible. This is a test of your application. It is not a claim about a API feature.

Review a correction as well. Withdraw the approved relationship and evaluate the profile, saved searches and alerts. The interface should explain the revised assessment without erasing the previous evidence.

Keep these fixtures free of live credentials and malicious files. The focus is to review relationship handling and not to deploy malware or contact an individual in the underground.

Report what the evidence supports

An analyst-facing result should describe the object matched and the relationship proposed. Displaying that two samples share an actor is less informative than saying they share a license fingerprint.

Assign unclear links to a reviewer. Record what evidence would lead to a different assessment.

Connect observations without making assumptions that every connection represents the same person. Leave sources, relationships, and connections reviewable.